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In this paper, we propose a new approach, called the matched shrunken subspace detector (MSSD), to target detection from hyperspectral images. The MSSD is developed by shrinking the abundance vectors of the target and background subspaces in the hypothesis models of the matched subspace detector (MSD), a popular subspace-based approach to target detection. The shrinkage is achieved by introducing simple l 2 -norm regularisation (also known as ridge regression or Tikhonov regularisation). We develop two types of MSSD, one with isotropic shrinkage and termed MSSD-i and the other with anisotropic shrinkage and termed MSSD-a. For these two new methods, we provide both the frequentist and Bayesian derivations. Experiments on a real hyperspectral imaging dataset called Hymap demonstrate that the proposed MSSD methods can outperform the original MSD for hyperspectral target detection. [ABSTRACT FROM AUTHOR]

This paper aims to propose a new hyperspectral target-detection method termed the matched subspace detector with interaction effects (MSDinter). The MSDinter introduces “interaction effects” terms into the popular matched subspace detector (MSD), from regression analysis in multivariate statistics and the bilinear mixing model in hyperspectral unmixing. In this way, the interaction between the target and the surrounding background, which should have but not yet been considered by the MSD, is modelled and estimated, such that superior performance of target detection can be achieved. Besides deriving the MSDinter methodologically, we also demonstrate its superiority empirically using two hyperspectral imaging datasets. [ABSTRACT FROM AUTHOR]

Abstract: For two-class discrimination, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562] claimed that, when covariance matrices of the two classes were unequal, a (class) unbalanced data set had a negative effect on the performance of linear discriminant analysis (LDA). Through re-balancing 10 real-world data sets, Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562] provided empirical evidence to support the claim using AUC (Area Under the receiver operating characteristic Curve) as the performance metric. We suggest that such a claim is vague if not misleading, there is no solid theoretical analysis presented in Xie and Qiu [The effect of imbalanced data sets on LDA: a theoretical and empirical analysis, Pattern Recognition 40 (2) (2007) 557–562], and AUC can lead to a quite different conclusion from that led to by misclassification error rate (ER) on the discrimination performance of LDA for unbalanced data sets. Our empirical and simulation studies suggest that, for LDA, the increase of the median of AUC (and thus the improvement of performance of LDA) from re-balancing is relatively small, while, in contrast, the increase of the median of ER (and thus the decline in performance of LDA) from re-balancing is relatively large. Therefore, from our study, there is no reliable empirical evidence to support the claim that a (class) unbalanced data set has a negative effect on the performance of LDA. In addition, re-balancing affects the performance of LDA for data sets with either equal or unequal covariance matrices, indicating that having unequal covariance matrices is not a key reason for the difference in performance between original and re-balanced data. [Copyright &y& Elsevier]

Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large amounts of aligned training pairs for various tasks; (2) the ambiguity of multiple possible outputs from a single input image; and (3) the lack of simultaneous training for multi-domain translation with a single network. Therefore in this paper, we propose a unified framework for learning to generate diverse outputs using unpaired training data and allow for simultaneous multi-domain translation via a single model. Moreover, we also observed from experiments that the implicit disentanglement of content and style could lead to undesirable results. Thus we investigate how to extract domain-level signal as explicit supervision so as to achieve better image-to-image translation. Extensive experiments show that the proposed method outperforms or is comparable with the state-of-the-art methods for various applications. [ABSTRACT FROM AUTHOR]

Highlights • Develop a new finger identification system to synchronously capture a two-modal image of dorsal finger vein and texture by a single camera. • Establish a corresponding two-modal hybrid images database of 210 finger samples. • Present a feature extraction method named by "Polarized depth-Weighted Binary Direction Coding", which explores features of two modalities simultaneously. Abstract Finger identification is increasingly popular in recent years. In this paper, we propose a new finger identification system to acquire a hybrid pattern of dorsal finger vein and texture in a single image by using only one camera. It effectively reduces the cost and volume of the imaging device to acquire multi-modal patterns. The hybrid pattern of dorsal finger vein and texture is both storage-saving and calculation-saving. As there was no existing method specially developed for this kind of pattern, we propose a new feature extraction method called "Polarized depth-Weighted Binary Direction Coding" (PWBDC). We also establish a new database of such hybrid images of 210 finger samples. Experimental results demonstrate that the proposed system and feature are not only storage and calculation saving, but more importantly effective for identification. The proposed PWBDC method performs well on both the newly established database of hybrid images and a popular public database of traditional finger vein images, superior to many established and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Quality assessment indexes play a fundamental role in the analysis of hyperspectral image (HSI) cubes. To assess the quality of an HSI cube, the structural similarity (SSIM) index has been widely applied in a band-by-band manner, as SSIM was originally designed for 2D images, and then the mean SSIM (MeanSSIM) index over all bands is adopted. MeanSSIM fails to accommodate the spectral structure which is a unique characteristic of HSI. Hence in this paper, we propose a new and simple multivariate SSIM (MvSSIM) index for HSI, by treating the pixel spectrum as a multivariate random vector. MvSSIM maintains SSIM’s ability to assess the spatial structural similarity via correlation between two images of the same band; and adds an ability to assess the spectral structural similarity via covariance among different bands. MvSSIM is well founded on multivariate statistics and can be easily implemented through simple sample statistics involving mean vectors, covariance matrices and cross-covariance matrices. Experiments show that MvSSIM is a proper quality assessment index for distorted HSIs with different kinds of degradations. [ABSTRACT FROM AUTHOR]

• We leverage the background variations in a finger vein image as a new auxiliary feature. • We propose a new descriptor named Intensity Orientation Vector (IOV) for background cues. • We propose a new supervised binary-feature learning scheme to jointly learn compact binary codes from both vein traits and background cues. • Extensive experiments demonstrate the effectiveness of IOV and the binary-feature learning scheme. Recently, finger vein based biometric authentication has attracted considerable attention due to its high efficiency and high security. However, most existing finger vein representation methods focus on vein traits while ignoring background cues, although background cues also convey identity information specific to each individual. In this paper, we leverage background intensity variations in finger vein images as new features to enrich discriminative representation, and accordingly propose a new descriptor named Intensity Orientation Vector (IOV). IOV, scaleable to reflect characteristics of finger tissues, offers additional informative cues for finger vein representation. Furthermore, we propose a new learning scheme named Semantic Similarity Preserved Discrete Binary Feature Learning (SSP-DBFL) for finger vein recognition. Unlike the most bimodal binary feature representation methods, SSP-DBFL preserves high-level semantic similarity in a common Hamming space to exploit the consensus between vein traits and background cues. Specifically, given a finger vein image, we first extract the direction difference vectors (DDV) as the main vein traits and the IOV as the auxiliary background cues. Subsequently, we jointly learn projection functions from these two types of features in a supervised manner, converting the two features into discriminative binary codes with their semantic similarity preserved. Finally, the binary codes are pooled into histogram-based vectors for finger vein representation. Extensive experiments are conducted on five widely used finger vein databases and demonstrate the effectiveness of our proposed IOV and SSP-DBFL. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel two-level hierarchical framework for three-dimensional (3D) skeleton-based action recognition, in order to tackle the challenges of high intra-class variance, movement speed variability and high computational costs of action recognition. In the first level, a new part-based clustering module is proposed. In this module, we introduce a part-based five-dimensional (5D) feature vector to explore the most relevant joints of body parts in each action sequence, upon which action sequences are automatically clustered and the high intra-class variance is mitigated. In the second level, there are two modules, motion feature extraction and action graphs. In the module of motion feature extraction, we utilize the cluster-relevant joints only and present a new statistical principle to decide the time scale of motion features, to reduce computational costs and adapt to variable movement speed. In the action graphs module, we exploit these 3D skeleton-based motion features to build action graphs, and devise a new score function based on maximum-likelihood estimation for action graph-based recognition. Experiments on the Microsoft Research Action3D dataset and the University of Texas Kinect Action dataset demonstrate that our method is superior or at least comparable to other state-of-the-art methods, achieving 95.56% recognition rate on the former dataset and 95.96% on the latter one. [ABSTRACT FROM AUTHOR]

Metric-based methods are one of the most common methods to solve the problem of few-shot image classification. However, traditional metric-based few-shot methods suffer from overfitting and local feature misalignment. The recently proposed feature reconstruction-based approach, which reconstructs query image features from the support set features of a given class and compares the distance between the original query features and the reconstructed query features as the classification criterion, effectively solves the feature misalignment problem. However, the issue of overfitting still has not been considered. To this end, we propose a self-reconstruction metric module for diversifying query features and a restrained cross-entropy loss for avoiding over-confident predictions. By introducing them, the proposed self-reconstruction network can effectively alleviate overfitting. Extensive experiments on five benchmark fine-grained datasets demonstrate that our proposed method achieves state-of-the-art performance on both 5-way 1-shot and 5-way 5-shot classification tasks. Code is available at https://github.com/liz-lut/SRM-main. • A novel fine-grained few-shot classification method is proposed to alleviate overfitting. • The proposed method uses self-reconstruction to increase the diversity of query features. • The proposed method introduces a restrained cross-entropy loss. • The proposed method generalizes well across five benchmark datasets. [ABSTRACT FROM AUTHOR]

Few-shot classification (FSC) is a highly challenging task, as only a small number of labeled samples are available when identifying new categories. Distance metric learning-based methods have emerged as a prominent approach to FSC, which typically use a distance function to measure the difference between query and support samples for identifying the class membership of the query sample. However, these methods simply treat each channel difference between query and support features equally when computing the class scores. Since different channels in the learned feature seek for different patterns, these distance metrics fail to consider that different channels are of different importance to FSC, and thus cannot accurately measure the similarity between samples. To address this issue, we propose a Q uery- C entric D istance M odulator (QCDM) to generate query-related weights for each channel difference adaptively. Specifically, since the distribution of difference between a query sample and all support samples in a particular channel can reflect the importance of this channel to the classification of the query sample, we take this difference vector as input and generate a query-specific channel weight through a meta-network. QCDM can guide FSC models to focus on discriminative channel differences and achieve better generalization. QCDM is a plug-and-play module that can be seamlessly integrated with existing distance metric learning-based FSC methods. Extensive experimental results indicate that our method can effectively improve the performance of distance metric learning-based FSC methods. The source code is available in https://github.com/Wu-Wenxiao/QCDM. • A plug-and-play query-centric distance modulator for distance metric learning-based FSC methods. • Weight generation through data inconsistency within each channel from different classes. • Decoupled from the process of feature extraction and integrated with any given backbone. [ABSTRACT FROM AUTHOR]